Solitude-Analysis

1 Data upload

2 Time management

3 ND removed, Converted to a Dataframe

#replace ND with 0

tr <- matrix(data = NA, ncol = ncol(dt[,c(1:46)]), nrow=nrow(dt))
colnames(tr) <- colnames(dt[,c(1:46)])
for (i in 12:46)
{
  tr[,c(i)] <- gsub(".*ND.*", 0, dt[,i])
}

for(i in 1:11)
{
  tr[,c(i)] <- dt[,c(i)]
}
   

#transform to dataframe
tr <- as.data.frame.matrix(tr) #A correct command to change the dataset to dataframe after transformations
tr[,12:46] <- sapply(tr[,12:46],as.numeric) # Change a character to numeric (double)
typeof(tr$Cu_concentration) # confirm the value is no longer a character

## [1] "double"

4 Head of the dataset

head(tr)

Data frame is now printed using kable.

Scientific_Name	Group	Plot	Sample_Name	Tube_No	Type_of_Sample	Total_Weight	Cup_No	pXRF_measurement_ID	ICP	Material	Cl_concentration	Cl_uncertainty	Ca_concentration	Ca_uncertainty	Ti_concentration	Ti_uncertainty	Cr_concentration	Cr_uncertainty	Mn_concentration	Mn_uncertainty	Fe_concentration	Fe_uncertainty	Co_concentration	Co_uncertainty	Ni_concentration	Ni_uncertainty	Cu_concentration	Cu_uncertainty	Zn_concentration	Zn_uncertainty	As_concentration	As_uncertainty	Se_concentration	Se_uncertainty	Cd_concentration	Cd_uncertainty	Re_concentration	Re_uncertainty	Hg_concentration	Hg_uncertainty	Tl_concentration	Tl_uncertainty	Pb_concentration	Pb_uncertainty	Substrate_RT
Dasyochloa pulchella	G2	P2	P2_E17_2	1;2	leaf-stem	0.471	1	2108	n	Plant	764	235	2583	142	64.8	14.9	4.6	3.8	0.0	3.5	417	18.6	0	1.7	0	1.0	0.0	3.1	0.0	1.4	3.7	1.2	0.0	0.6	0	4.2	0.0	1.6	0	0.5	0	0.4	0	1.6	0.0280626
Dasyochloa pulchella	G2	P2	P2_E17_1	5;6	leaf-stem	0.434	3	2110	n	Plant	745	225	2655	139	86.4	16.2	0.0	2.1	21.8	5.3	667	22.0	0	1.8	0	1.1	28.8	3.2	10.6	2.0	1.2	0.7	0.8	0.6	0	4.0	1.8	1.8	0	0.4	0	0.6	0	1.5	0.0305714
Dasyochloa pulchella	G2	P5	P5_60_2_3	9;10	leaf-stem	0.49	5	2112	n	Plant	675	227	3254	160	81.4	16.7	4.4	4.0	0.0	3.7	521	18.7	0	1.6	0	0.9	0.0	2.6	0.0	1.0	4.3	0.3	0.0	0.3	0	2.6	0.0	1.2	0	0.3	0	0.4	0	0.8	0.0542722
Dasyochloa pulchella	G2	P2	P2_E17	15;16	leaf-stem	0.443	6	2116	n	Plant	1502	318	2579	144	54.5	14.1	3.8	3.8	0.0	3.9	470	18.3	0	1.6	0	0.9	0.0	1.4	19.3	3.3	3.7	0.3	0.0	0.4	0	5.6	0.0	1.4	0	0.4	0	0.4	0	0.9	0.0420151
Dasyochloa pulchella	G2	P1	P1_37_2	13;14	leaf-stem	0.562	8	2115	n	Plant	771	244	6766	233	143.0	21.7	0.0	2.7	0.0	4.8	917	24.8	0	2.0	0	0.9	73.8	5.0	0.0	0.9	4.1	0.3	2.3	0.6	0	5.2	0.0	1.3	0	0.4	0	0.4	0	0.8	0.0552199
Dasyochloa pulchella	G2	P5	P5_60_2_1	11	leaf-stem	0.17	9	2113	n	Plant	1029	265	2637	142	28.2	11.2	3.0	3.0	0.0	3.6	129	10.4	0	1.2	0	0.9	0.0	1.7	0.0	1.2	2.9	1.0	0.0	0.3	0	7.2	0.0	1.4	0	0.4	0	0.4	0	1.5	0.0320037

5 Subsets and wrangling

#Filtering with tydeverse library
dt_plants <- filter(tr,  Scientific_Name != 'QA_Sample')

P1 <- filter(dt_plants, Plot == "P1")
P2 <- filter(dt_plants, Plot == "P2")
P5 <- filter(dt_plants, Plot == "P5")
P6 <- filter(dt_plants, Plot == "P6")
P125 <- filter(dt_plants, Plot != "P6")

Se_best <- subset(dt_plants, Scientific_Name == 'Isocoma cf. tenuisecta' | Scientific_Name == 'Populus fremontii' | Scientific_Name == 'Senegalia (Acacia) greggii' )

Re_best <- subset(dt_plants, Scientific_Name == 'Isocoma cf. tenuisecta' | Scientific_Name == 'Baccharis sarothroides' | Scientific_Name == 'Senegalia (Acacia) greggii'| Scientific_Name == 'Nultuma (Prosopis) velutina' | Scientific_Name == 'Mimosa biuncifera (=aculeaticarpa)' | Scientific_Name == 'Fraxinus velutina'| Scientific_Name == 'Datura wrightii' )


# Dropping uncertainty columns for PCA analysis

dt_plants_nounc = select(dt_plants, -Cl_uncertainty,-Ca_uncertainty, -Ti_uncertainty,
                         -Cr_uncertainty, -Mn_uncertainty, -Fe_uncertainty, -Ni_uncertainty, -Cu_uncertainty,
                         -Zn_uncertainty, -As_uncertainty, -Se_uncertainty, -Cd_uncertainty, -Re_uncertainty, -Hg_uncertainty, -Co_uncertainty,
                         -Tl_uncertainty, -Pb_uncertainty, -Substrate_RT)

dt_plants_nounc = select(dt_plants_nounc, -Hg_concentration, -Tl_concentration, -Pb_concentration, -Ni_concentration, -Co_concentration)


#Filtering plants By Plot with subset function

dt_plants_nounc1 <- subset(dt_plants_nounc, Plot=="P1")
dt_plants_nounc2 <- subset(dt_plants_nounc, Plot=="P2")
dt_plants_nounc5 <- subset(dt_plants_nounc, Plot=="P5")
dt_plants_nounc6 <- subset(dt_plants_nounc, Plot=="P6")
dt_plants_nounce15 <- subset(dt_plants_nounc, Plot=="P1" | Plot=="P5")
dt_plants_nounce125 <- subset(dt_plants_nounc, Plot=="P1" | Plot=="P5" | Plot=="P2")

#Removing _concentration from column names

colnames(dt_plants_nounce125)[12] <- "Cl"
colnames(dt_plants_nounce125)[13] <- "Ca"
colnames(dt_plants_nounce125)[14] <- "Ti"
colnames(dt_plants_nounce125)[15] <- "Cr"
colnames(dt_plants_nounce125)[16] <- "Mn"
colnames(dt_plants_nounce125)[17] <- "Fe"
colnames(dt_plants_nounce125)[18] <- "Cu"
colnames(dt_plants_nounce125)[19] <- "Zn"
colnames(dt_plants_nounce125)[20] <- "As"
colnames(dt_plants_nounce125)[21] <- "Se"
colnames(dt_plants_nounce125)[22] <- "Cd"
colnames(dt_plants_nounce125)[23] <- "Re"

colnames(dt_plants_nounc6)[12] <- "Cl"
colnames(dt_plants_nounc6)[13] <- "Ca"
colnames(dt_plants_nounc6)[14] <- "Ti"
colnames(dt_plants_nounc6)[15] <- "Cr"
colnames(dt_plants_nounc6)[16] <- "Mn"
colnames(dt_plants_nounc6)[17] <- "Fe"
colnames(dt_plants_nounc6)[18] <- "Cu"
colnames(dt_plants_nounc6)[19] <- "Zn"
colnames(dt_plants_nounc6)[20] <- "As"
colnames(dt_plants_nounc6)[21] <- "Se"
colnames(dt_plants_nounc6)[22] <- "Cd"
colnames(dt_plants_nounc6)[23] <- "Re"

6 Data Visualization

6.1 Boxplots - Cu - All plots

Cu_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Cu_concentration, FUN = median),
                                    y = Cu_concentration, group=Scientific_Name)) +
  geom_boxplot()+
  geom_jitter(aes(colour = Plot), size=1.1) +
  coord_flip()+
  scale_color_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E")) +
  scale_x_discrete(guide = guide_axis(angle = 0))+
  scale_y_continuous(limits = c(0, 600), breaks = seq(0, 800, by = 50)) +
  theme_classic()+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=10),
        axis.title.x = element_text(size = 13),
        axis.text.y = element_text(size=10.5, face="italic"),
        axis.title.y = element_blank(),
        legend.key.size = unit(1, "lines"),
        legend.text = element_text(size = 10)) +
  guides(colour = guide_legend(override.aes = list(size = 3.5)))+
  ylab("Copper Concentration (ppm)")
Cu_AllPlots

6.2 Boxplots - Re - All Plots

Re_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Re_concentration, FUN = median),
                                    y = Re_concentration, group=Scientific_Name)) +
  geom_boxplot()+
  geom_jitter(aes(colour = Plot), size=1.1) +
  coord_flip()+
  scale_color_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E")) +
  scale_x_discrete(guide = guide_axis(angle = 0))+
  scale_y_continuous(limits = c(0, 110), breaks = seq(0, 110, by = 10)) +
  theme_classic()+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=10),
        axis.title.x = element_text(size = 13),
        axis.text.y = element_text(size=10.5, face="italic"),
        axis.title.y = element_blank(),
        legend.key.size = unit(1, "lines"),
        legend.text = element_text(size = 10)) +
  guides(colour = guide_legend(override.aes = list(size = 3.5)))+
  ylab('Rhenium concentration (ppm)')
Re_AllPlots

6.3 Boxplots - Re - Selected species

Re_box <- ggplot(Re_best, aes(x = reorder(Scientific_Name, Re_concentration, FUN = median), y = Re_concentration, fill=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 45))+
  geom_jitter(color="#85b8bc", size=2, alpha=0.9) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  #scale_fill_manual(values = c("", "", "", "", "", "","" ))
  scale_fill_manual(values = c("#4b2866", "#c7abdd", "#a578c9", "#381e4c", "#8347b2", "#5d327f","#251433" ))
  #scale_fill_brewer(palette = "Greens")

Re_box

6.4 Boxplots - Zn - All Plots

Zn_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Zn_concentration, FUN = median), y = Zn_concentration, group=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  #theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 0))+
  geom_jitter(aes(colour = Plot), size=1) +
  #ylim(0,600)+
  coord_flip()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Zn_AllPlots

6.5 Boxplots - Se - All Plots

Se_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Se_concentration, FUN = median), y = Se_concentration, group=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  #theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 0))+
  geom_jitter(aes(colour = Plot), size=1) +
  ylim(0,60)+
  coord_flip()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Se_AllPlots

6.6 Boxplots - Se - Selected species

Se_box <- ggplot(Se_best, aes(x = reorder(Scientific_Name, Se_concentration, FUN=median), y = Se_concentration, fill=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 45))+
  geom_jitter(color="#85b8bc", size=3, alpha=0.9) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  scale_fill_manual(values = c("#251433", "#c7abdd", "#8347b2"))
Se_box

6.7 Boxplots - Cu - Plot 6

Plants collected at the plot 6 were growing directly on the mine tailings that were exposed on the area of 100 x 100 m. Shrubs were also collected in the close vicinity to the tailings given their rooting depths.

Plot 6

Cu_Plot6 <- ggplot(P6, aes(x = reorder(Scientific_Name, Cu_concentration, FUN = median), y = Cu_concentration, group=Scientific_Name)) +
  geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
  #theme(legend.position = "none")+
  scale_x_discrete(guide = guide_axis(angle = 0))+
  geom_jitter(aes(colour = Plot), size=1.6) +
  ylim(0,600)+
  coord_flip()+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
  #scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E", "#38A6A5", "#73AF48", "#EDAD08", "#CC503E", "#38A6A5", "#73AF48", "#EDAD08"))
Cu_Plot6

7 Soil Table

8 PCA Analysis

8.1 Creating principal components

require(stats)
myPr1 <- prcomp(dt_plants_nounc1[,12:23], scale=TRUE)
myPr2 <- prcomp(dt_plants_nounc2[,12:23], scale=TRUE)
myPr5 <- prcomp(dt_plants_nounc5[,12:23], scale=TRUE)
myPr6 <- prcomp(dt_plants_nounc6[,12:23], scale=TRUE)
myPr15 <- prcomp(dt_plants_nounce15[,12:23], scale=TRUE)
myPr125 <- prcomp(dt_plants_nounce125[,12:23], scale=TRUE) # it was not working because the scale was FALSE

8.2 Biplots1

biplot(myPr1, scale=0)

biplot(myPr125, scale=0)

8.3 Biplots2 - Plot 1, 2 and 5

biplot125 <- biplot(myPr125,
             col=c('blue', 'red'),
             cex=c(0.8, 0.8),
             xlim=c(-.4, .4),
             main='PCA Results',
             expand=1.2)

8.4 Biplots2 - Plot 6

biplot6 <-  biplot(myPr6,
            col=c('blue', 'red'),
            cex=c(0.8, 0.8),
            xlim=c(-.4, .4),
            main='PCA Results',
            expand=1.2)

8.5 Bind dataframes with PC1 and PC2 for each plot

dt_plants1 <- cbind(dt_plants_nounc1, myPr1$x[,1:2])
dt_plants2 <- cbind(dt_plants_nounc2, myPr2$x[,1:2])
dt_plants5 <- cbind(dt_plants_nounc5, myPr5$x[,1:2])
dt_plants6 <- cbind(dt_plants_nounc6, myPr6$x[,1:2])
dt_plants15 <- cbind(dt_plants_nounce15, myPr15$x[,1:2])

8.6 PCA All plots

# Plot for all plot
myPr_all <- prcomp(dt_plants_nounc[,12:23], scale=TRUE)
dt_plants_all <- cbind(dt_plants_nounc, myPr_all$x[,1:2])

ggplot(dt_plants_all, aes(PC1, PC2, col=Plot, fill=Plot))+
  stat_ellipse(geom="polygon", col="black", alpha=0.5)+
  theme_classic()+
  geom_point(shape=21, col="black")

8.7 Variances across principle components

plot(myPr125, type="l")

summary(myPr1)

## Importance of components:
##                           PC1    PC2    PC3     PC4     PC5    PC6     PC7
## Standard deviation     1.7892 1.6134 1.2723 1.09515 1.00262 0.8604 0.71435
## Proportion of Variance 0.2668 0.2169 0.1349 0.09995 0.08377 0.0617 0.04252
## Cumulative Proportion  0.2668 0.4837 0.6186 0.71853 0.80230 0.8640 0.90652
##                            PC8     PC9   PC10    PC11    PC12
## Standard deviation     0.63622 0.59865 0.4436 0.30908 0.25739
## Proportion of Variance 0.03373 0.02987 0.0164 0.00796 0.00552
## Cumulative Proportion  0.94025 0.97012 0.9865 0.99448 1.00000

9 Partial Least Square Discriminant Analysis (PLS-DA)

library(readr)
library(dplyr)
library(tidyr)

## 
## Attaching package: 'tidyr'

## The following objects are masked from 'package:reshape':
## 
##     expand, smiths

## The following object is masked from 'package:reshape2':
## 
##     smiths

library(ropls)




dt_plants_nounc_3 <- dt_plants_nounc |> select(-Scientific_Name, -Group, -Plot, -Sample_Name, -Tube_No, -Type_of_Sample, -Cup_No, -pXRF_measurement_ID, -ICP, -Material)

typeof(dt_plants_nounc_3$Total_Weight)

## [1] "character"

dt_plants_nounc_3[,1] <- sapply(dt_plants_nounc_3[,1],as.numeric)

dt_nounc_PCA <- opls(x=dt_plants_nounc_3)

## PCA
## 226 samples x 13 variables
## standard scaling of predictors
##       R2X(cum) pre ort
## Total    0.558   3   0

plot(dt_nounc_PCA)

plot(dt_nounc_PCA, typeVc ="x-score", parAsColFcVn=dt_plants_nounc$Plot)

dt_opls <-opls(dt_plants_nounc_3, dt_plants_nounc$Plot)

## PLS-DA
## 226 samples x 13 variables and 1 response
## standard scaling of predictors and response(s)
##       R2X(cum) R2Y(cum) Q2(cum) RMSEE pre ort pR2Y  pQ2
## Total    0.518    0.158   0.113 0.402   3   0 0.05 0.05

summary(dt_opls)

## Length  Class   Mode 
##      1   opls     S4

##PCA

plot(dt_nounc_PCA, typeVc ="x-score", parAsColFcVn=dt_plants_nounc$Cu)

dt_opls <-opls(dt_plants_nounc_3, dt_plants_nounc$Cu)

## PCA
## 226 samples x 13 variables
## standard scaling of predictors
##       R2X(cum) pre ort
## Total    0.558   3   0

10 Redundancy Analysis (RDA) Biplot

dt_plants_trimmed <- dt_plants[c(-2,-4,-5,-6,-8,-10,-11, -24, -25, -40, -41, -42, -43, -44, -45, -seq(11,45,by=2))]
dt_plants_trimmed[,3] <- sapply(dt_plants_trimmed[,3],as.numeric)
library(vegan)

## Loading required package: permute

## Loading required package: lattice

## Registered S3 methods overwritten by 'vegan':
##   method      from
##   plot.rda    klaR
##   predict.rda klaR
##   print.rda   klaR

## This is vegan 2.6-4

# Create a matrix of the environmental variables (columns 5 to 18)
env_mat <- as.matrix(dt_plants_trimmed[,5:18])

# Create a data frame of the response variables (weight and thickness)
resp_df <- data.frame(weight = dt_plants_trimmed[,3], thickness = dt_plants_trimmed[,18])

# Perform RDA
rda_result <- rda(env_mat, resp_df)

# Print the RDA results
summary(rda_result)

## 
## Call:
## rda(X = env_mat, Y = resp_df) 
## 
## Partitioning of variance:
##                 Inertia Proportion
## Total         121406484    1.00000
## Constrained     1531857    0.01262
## Unconstrained 119874625    0.98738
## 
## Eigenvalues, and their contribution to the variance 
## 
## Importance of components:
##                            RDA1      RDA2       PC1       PC2       PC3
## Eigenvalue            1.510e+06 2.150e+04 1.177e+08 2.056e+06 9.631e+04
## Proportion Explained  1.244e-02 1.771e-04 9.696e-01 1.694e-02 7.933e-04
## Cumulative Proportion 1.244e-02 1.262e-02 9.822e-01 9.992e-01 1.000e+00
##                             PC4       PC5       PC6       PC7       PC8
## Eigenvalue            3.911e+03 8.272e+02 3.950e+02 1.908e+02 6.841e+01
## Proportion Explained  3.221e-05 6.813e-06 3.253e-06 1.572e-06 5.635e-07
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
##                             PC9      PC10      PC11
## Eigenvalue            1.485e+01 1.433e+01 5.563e+00
## Proportion Explained  1.223e-07 1.180e-07 4.582e-08
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00
## 
## Accumulated constrained eigenvalues
## Importance of components:
##                           RDA1      RDA2
## Eigenvalue            1.51e+06 2.150e+04
## Proportion Explained  9.86e-01 1.403e-02
## Cumulative Proportion 9.86e-01 1.000e+00
## 
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:  406.5426 
## 
## 
## Species scores
## 
##                        RDA1       RDA2       PC1        PC2        PC3
## Cl_concentration -1.118e+00 -2.6132416 3.913e+00  5.290e+01 -3.458e-02
## Ca_concentration -4.527e+01  0.2982983 4.003e+02 -5.182e-01 -7.806e-02
## Ti_concentration -1.996e-01 -0.8915015 4.554e-01  3.743e-02  1.408e+00
## Cr_concentration  3.508e-03 -0.1006886 1.345e-02  1.917e-02  5.380e-03
## Mn_concentration -4.304e-02  0.2687386 3.385e-01 -5.274e-02 -1.114e-02
## Fe_concentration -2.259e+00 -4.6223101 2.690e+00  1.426e-01  1.129e+01
## Ni_concentration  7.653e-04  0.0016839 4.491e-04 -7.096e-04  7.266e-04
## Cu_concentration  1.159e-01 -0.2915491 2.933e-01  9.958e-02  1.258e+00
## Zn_concentration  6.549e-02  0.1202517 4.283e-02  1.816e-02 -2.305e-02
## As_concentration -9.716e-03 -0.0580838 9.539e-03  5.080e-03  1.150e-02
## Se_concentration -3.236e-03  0.0208173 2.390e-02  3.584e-03  3.048e-03
## Cd_concentration -1.077e-02 -0.0022981 7.833e-03  1.386e-02 -5.672e-05
## Re_concentration -2.004e-03  0.0158494 5.890e-02 -2.211e-03 -2.706e-02
## Substrate_RT      3.390e-04  0.0007463 5.078e-20  8.679e-21 -1.161e-20
##                         PC4
## Cl_concentration  3.409e-03
## Ca_concentration  3.582e-04
## Ti_concentration -4.713e-01
## Cr_concentration  7.629e-03
## Mn_concentration -3.109e-01
## Fe_concentration  3.046e-01
## Ni_concentration -3.964e-03
## Cu_concentration -2.213e+00
## Zn_concentration -1.224e-01
## As_concentration  4.268e-03
## Se_concentration -1.589e-02
## Cd_concentration  5.602e-04
## Re_concentration -3.057e-02
## Substrate_RT     -2.038e-20
## 
## 
## Site scores (weighted sums of species scores)
## 
##            RDA1       RDA2        PC1       PC2       PC3       PC4
## sit1    254.579  -79.59394 -34.615566 -13.60594   5.80276   13.7411
## sit2    252.729 -117.32968 -33.206407 -13.90277  28.98687   14.2441
## sit3    239.737  -81.73980 -26.026203 -14.25518  27.86951   28.7668
## sit4    254.209 -153.51181 -30.372792   0.91158  17.05784   23.9373
## sit5    161.917 -119.65657 -18.029966 -12.93695  61.87626   14.8337
## sit6    253.566  -56.09576 -29.027590  -8.82741 -18.19470    7.0818
## sit7    242.813  -87.07001 -29.807868  -8.76044   8.65178   18.8549
## sit8    226.472 -218.74636 -31.997624  -7.30947  72.30309   52.9857
## sit9    249.785 -167.77662 -26.346705  20.69576 -13.54343    9.8384
## sit10   210.716 -331.60059 -20.130961  57.99842  -8.90298   15.7071
## sit11  -187.813  -40.15404  16.117741 -26.87476  98.43987   75.5024
## sit12  -727.041  322.94384  82.229273 -26.35216  21.68294   27.4388
## sit13  -331.634 -206.52794  35.002377  56.45269   8.80916   22.5313
## sit14   174.799  -33.40308 -25.770464 -15.60207  -9.92309    8.8928
## sit15  -650.223  143.24273  73.131123  -3.41385  54.55562   37.4839
## sit16  -386.750   96.39143  44.672582 -25.25865  69.94003   46.0739
## sit17  -568.919  221.75330  63.664977 -16.04563  18.87576    4.0106
## sit18   246.298    6.37027 -26.549628 -15.13713  -5.35203    8.0530
## sit19   238.625  -63.83978 -26.453954  -0.19923  -3.35171    8.2213
## sit20   201.290  -56.69459 -19.567087   1.94614  -9.74758    8.9149
## sit21   237.895 -110.90015 -26.744408   9.54498  -5.42055    9.8875
## sit22   254.848  -35.89373 -25.465689  -6.34791   5.48671   12.8965
## sit23   238.569   -4.04463 -23.223544 -15.59292   1.03484   -7.9953
## sit24   256.238  -24.82954 -27.221646 -10.46128  -3.33351    7.4014
## sit25  -145.550  172.93644  13.787954 -15.39409  -9.28190  -31.1128
## sit26  -169.225  165.31598  18.986389 -12.17421 -12.90751  -20.0969
## sit27    17.739   -0.53960   0.014841   5.93332  -4.95943   -9.9670
## sit28   -46.025  127.81802   4.759508 -17.10916  -5.09428  -26.2970
## sit29    29.047   84.00206  -3.226319 -21.87481  -5.57393   13.4324
## sit30  -202.868 -659.07832  19.944600 156.60820 -11.16897    8.6405
## sit31   -17.621 -114.89330   2.904159  20.30124  -4.10274   20.8865
## sit32  -222.003  -85.96149  21.743092  32.69740   1.64625    2.6726
## sit33  -173.736   -4.96110  19.476734  15.38109   0.89773   11.5852
## sit34  -305.814   14.42813  33.649070  12.06702  21.64288   15.4412
## sit35   -27.124   63.02206   1.226710 -12.00154  -0.59756    8.8178
## sit36     5.747   25.83490  -3.945567  -7.45852   0.42666    7.0758
## sit37  -114.651   -6.65658  11.873439  12.63759 -10.19570   11.7620
## sit38   123.662  -38.76633 -17.275638  -8.04522 -12.76736    7.3408
## sit39  -605.623  173.57852  66.536886 -18.52929  52.72174   42.6211
## sit40   264.234  -46.61973 -28.125225  -6.08364 -12.41944    7.8348
## sit41   221.469 -147.03593 -22.182370  19.30244  -3.69415   13.6072
## sit42   111.856 -331.80323 -17.648094  65.74958 -18.14612   -1.9109
## sit43    77.593  -28.01550  -8.587916  -0.67307  16.22716   -4.3074
## sit44   180.069   25.86089 -24.429569 -22.01992 -27.03666    0.5598
## sit45   304.539   33.68750 -39.619055 -29.19337 -41.65585   -6.9115
## sit46   205.110   18.01443 -23.390911 -20.96546 -18.22905    0.7580
## sit47   139.581   53.58411 -18.333495 -19.98780 -32.10103   -3.9865
## sit48    56.128  131.97119  -2.312555 -27.46073  -3.27428    4.5116
## sit49   173.284  -53.80066 -20.027045  -0.76186 -18.49276  -12.9132
## sit50   284.129   39.43733 -37.102009 -29.28574 -39.68111   -9.2589
## sit51   206.494  -78.41755 -20.414612   4.70982   0.26734   10.2154
## sit52   206.024  -32.00430 -19.884322  -5.42725   0.81945   12.4216
## sit53   212.724 -182.92100 -20.696022  27.45613   0.75272   15.5415
## sit54   241.980 -158.57763 -24.969582  19.81113  -2.20130   10.0741
## sit55   211.145 -178.23999 -22.005423  26.91560  -7.24850   11.6578
## sit56   191.902 -193.92546 -24.902517  29.35027 -18.51768    0.4056
## sit57   205.914 -137.29040 -21.843841  17.25672  -3.85881    9.0853
## sit58   227.441 -127.28076 -21.159811  14.88563  -3.44153   13.9615
## sit59   167.869  -89.61862 -15.260086  10.67650   0.40807    8.2649
## sit60    76.630  -85.25906  -6.637246  15.11519  -1.02106   -2.5869
## sit61    50.643   66.07586  -1.853287 -11.27861  -2.26603    3.7574
## sit62    84.227  -16.07175  -6.469645   1.31703   0.55604    8.6764
## sit63   108.977    0.03648  -9.800129  -2.56670 -10.03996    2.1925
## sit64   121.144  -28.83753 -10.160265   3.30702  -1.22049    7.8079
## sit65   -82.445  -54.50279   7.670754  22.03613   9.18441    1.5559
## sit66  -482.089  276.02866  52.746207 -15.05392  -5.98468   -6.0943
## sit67  -132.167   24.03040  11.101474  10.31347 -32.31624    2.5416
## sit68  -148.404   64.52020  14.912086   4.80104 -21.38739   -2.8086
## sit69  -516.542  311.34034  54.577673 -16.56418 -18.03473  -21.1904
## sit70   -79.089 -221.14105   6.679470  50.73995 -18.33752    7.0746
## sit71    91.988  -42.19449  -5.912381  -3.68513  25.12218    9.1438
## sit72    53.641   35.91342  -2.402636 -11.18733   8.18624   12.9741
## sit73    56.726   58.81808  -5.281617 -20.07221  13.06854   16.5787
## sit74   111.385   59.60788  -8.327878 -17.66682   1.96552   11.0302
## sit75    76.946   -2.37716  -8.084181 -10.48797  36.57652    2.2069
## sit76   -16.551  177.70702   1.354422 -28.17276 -14.58806    7.7783
## sit77  -132.800  220.36496  10.831780 -27.35963   1.21613    5.3263
## sit78  -140.738  223.06683   9.242539 -25.96804  12.56010   -3.2297
## sit79   -51.756  182.11702   1.185210 -26.75998   3.04667    1.0673
## sit80   -38.472   85.49532   2.588156 -10.12348 -27.49221   -7.7807
## sit81  -123.347  126.68213  13.823415 -11.81992 -12.60819   -2.1429
## sit82  -144.389   95.91495  14.232097  -5.49700   4.73625  -10.9238
## sit83   -31.442   61.04834   6.515954  -3.70180  -3.88126  -13.6804
## sit84    -9.461   77.86515   6.594701 -10.32944 -17.98949    2.3436
## sit85   -45.717  185.38093   2.658559 -27.99957  -9.73912  -12.1573
## sit86    39.167  -53.33539  -6.107252  16.73154  -6.69159    4.5667
## sit87   150.806 -104.19684 -21.096727  17.60074  -2.90268   -2.2684
## sit88    76.988 -201.65480 -13.912810  44.92209  -2.49988   -7.6995
## sit89    -2.049  -67.26187   3.639454  16.35456   4.90268   -5.2059
## sit90    -3.448   11.14001   5.014643   1.24465   1.22981    6.9281
## sit91    33.437   15.92728  -0.875059  -2.78651   0.86674  -17.4551
## sit92    55.522    4.38652  -5.814775   0.95951 -11.04774    4.9160
## sit93    23.831    3.85582   2.943887  -1.48965   5.26501  -37.0660
## sit94    38.252   76.02191  -7.037579 -10.74776   0.16535    4.2906
## sit95    67.996   73.39566  -9.258813 -12.70002  12.57049    3.9510
## sit96   -16.726  106.62242   3.492950 -10.72180  11.71485    8.9775
## sit97   240.603 -241.60657 -29.820213  33.04971 -14.48961  -11.3598
## sit98   187.265   -0.81771 -25.662064 -13.87727 -21.84835   -9.9688
## sit99  -148.519  185.93260  18.760273 -18.37253 -21.00470    7.2870
## sit100 -596.611   75.55122  70.295616  38.93324 -34.89065   14.3496
## sit101 -168.908  -52.48085  21.024237  27.74468   0.20697  -10.7570
## sit102    6.815 -310.59399   4.232789  68.41588  -7.03167   -5.9667
## sit103  122.846 -132.78635 -10.834725  18.25573 -11.18394  -55.2803
## sit104  200.466  -64.34813 -18.069818  -3.32289   4.75440  -40.5778
## sit105  245.156  -89.67004 -29.663812 -10.15060   7.66462   18.5437
## sit106  255.212  -66.84026 -38.162957 -21.25874  16.30463   11.0192
## sit107  262.550 -112.27131 -31.573637  -8.12855   8.16954   14.7728
## sit108  152.687 -248.14725 -14.741203  45.53799 -10.15558   14.3247
## sit109  245.374 -232.12551 -24.801573  34.35524 -12.75188   12.0942
## sit110  272.181   -7.43471 -31.403832 -16.63648 -27.80567    2.0533
## sit111 -748.288  300.45656  86.041159 -24.10835  48.47036   38.5162
## sit112 -524.078  -27.16805  62.738736  15.11165  76.33183   53.3936
## sit113 -745.184  497.69529  84.192780 -37.02676 -47.60523    7.9333
## sit114  -80.380  104.97444   3.526360 -21.53936 -19.65551   -1.5093
## sit115  245.749   -3.48213 -27.160029 -14.29068 -12.16914    6.9006
## sit116  245.540  -81.53818 -23.618211   3.25286  -0.53172   13.5478
## sit117  214.273  -16.79464 -22.855333  -6.25063   4.14047    8.5796
## sit118  227.193   27.13234 -23.845995 -20.87065  -5.78601  -15.7749
## sit119  259.929   18.67681 -27.219621 -19.35483  -2.47223    9.5263
## sit120 -286.008   43.50456  30.292833  23.54810  -8.26241  -12.7457
## sit121 -266.423  137.59927  32.024667   1.30952  -5.39993  -21.0686
## sit122 -155.777  143.72281  18.691804  -9.99572  -4.53641  -45.7423
## sit123 -184.835  128.85486  19.861858  -5.17628 -21.84975  -37.0437
## sit124 -224.803  233.09066  27.490741 -23.19350  -9.51961  -53.1410
## sit125    1.986    8.86899  -0.716648  -2.63793 -18.25159   11.4839
## sit126 -119.790   25.87020  11.473843   1.92861   1.48166    9.4709
## sit127  120.270  -20.35288 -16.203925  -7.91288 -11.57844    7.0135
## sit128   54.306   30.37822  -7.467265 -14.90368  -9.45420   12.8983
## sit129 -615.596  271.16566  71.511922  -2.46936  -5.17572   12.4325
## sit130   93.166 -157.04570  -7.991360  14.53331  26.57292    8.7169
## sit131  104.753 -258.59775 -10.850356  41.37221  27.91371   12.3642
## sit132   96.109 -207.14543  -9.782928  27.85297  34.18142    8.6709
## sit133   32.146   52.74264   2.292207 -16.92975   4.09174   10.6937
## sit134  216.766  -36.42936 -25.582320 -12.62906 -14.98924  -10.3041
## sit135   96.517   17.89711  -6.503890  -8.62349   7.63677   -1.2157
## sit136  127.231 -100.40181 -16.622612   3.39158  -7.88948  -48.0986
## sit137  109.245 -569.51000 -17.146464  66.20047  98.65932 -105.2963
## sit138  111.358 -484.02872 -17.658369  53.00357  86.13424  -56.0367
## sit139  129.431 -360.43477 -16.694791  48.24575  23.37100  -39.7338
## sit140 -852.882  310.33026  97.810182  13.15595 -36.43033    5.9120
## sit141 -839.543   98.29933  96.835422  58.20712 -28.36290   10.4112
## sit142 -774.209  240.13430  90.925602  19.86314 -19.48343    9.6463
## sit143  193.702 -170.86767 -19.505826  26.16411  -5.81701   10.8893
## sit144  246.615 -100.80957 -26.330919   6.37332  -8.72407    8.8134
## sit145  217.233 -121.79368 -19.890729  15.29298  -1.43271   13.3713
## sit146   85.444  -20.30047  -7.359657   2.49753  -6.69355    8.0563
## sit147  122.707  -98.19084 -12.221939  14.99642 -15.89430    0.8485
## sit148  122.422 -148.45792 -10.885680  27.55135  -0.33512    6.8980
## sit149 -433.520  240.23359  47.089023 -13.76842 -23.58467   -7.0546
## sit150 -201.921   96.58420  21.005865   2.08384 -26.57443    5.0691
## sit151 -202.392   66.37729  21.998787   6.09403 -13.37921   -6.2058
## sit152  -92.325  172.53034   8.511512 -22.39173 -21.69025    5.1730
## sit153  -15.117  167.60688   2.115569 -28.56064 -12.34334    9.7445
## sit154   24.781   57.33488  -4.329275 -22.09204  -1.71271    8.7424
## sit155 -125.095 -146.31252  12.132166  39.31498  -0.97049   -1.8407
## sit156 -148.572   29.69314  15.191323   6.64274  -4.08982   -1.7061
## sit157 -234.242  150.40614  23.074373 -25.35972   9.53387  -51.1322
## sit158 -269.612  225.65484  33.483273 -31.39909   9.33721  -82.7049
## sit159 -158.872  157.08838  21.875580 -28.94439  34.80909  -62.7164
## sit160  -45.248 -369.96358   1.804031  83.38880 -32.14035    7.0966
## sit161   11.465  -69.91497  -5.047810  -5.03074  16.59717   31.2890
## sit162 -194.208  156.78833  22.705748 -22.39116  11.41905   -6.4365
## sit163 -216.498    1.80233  24.074962  -9.93871  48.03313   35.8123
## sit164   51.094   39.14833  -2.579173 -13.30088   7.37183   11.7534
## sit165  125.165   48.18890  -7.857260 -15.04168   5.55472   15.3621
## sit166   33.707  152.78890   2.524112 -27.78782  -8.83197   15.0032
## sit167  154.415  100.16155 -16.685978 -26.48838  -8.03291    8.6156
## sit168  110.363  119.00874 -12.119048 -27.35236 -12.48597    5.6134
## sit169   20.703  101.51984  -1.332440 -20.43590  -3.64103    2.9770
## sit170  -82.389 -519.96531   7.300025 117.54326  -6.43064   10.9739
## sit171   55.422  127.54830  -6.940609 -27.59761  -3.57134    1.7760
## sit172 -340.331  286.77348  38.465005 -26.71037 -33.26481  -22.5049
## sit173 -328.579  313.48043  41.205348 -31.68503 -22.21377  -20.8197
## sit174 -486.852  386.50857  53.471437 -31.64695 -18.27292  -21.9065
## sit175  -96.810  205.72062  12.250673 -27.16387   3.38871    7.9124
## sit176  -50.980  181.95707  -2.888454 -25.35997  12.00751    2.4280
## sit177 -142.930  224.85085  19.063073 -29.57447 -12.61085    4.4836
## sit178  165.616   90.91398 -20.225802 -27.50555 -15.97260    5.4895
## sit179  -14.029   60.14635   1.625452  -4.54566  -5.72596   -1.8197
## sit180  -10.954   82.17835  -3.015140 -12.58333 -31.83507  -12.4073
## sit181  -66.417  137.05567   5.817779 -17.18569 -18.97604   -9.3211
## sit182  153.246 -261.66388 -16.638184  49.22149  -8.02597    6.1885
## sit183  124.056 -181.54589 -18.770932  35.40456  -6.90481   -0.8337
## sit184   99.008  -30.97522 -18.009435   7.20354  -1.50738   -4.1342
## sit185  212.701  -80.08197 -20.256920   2.60538   4.17146   10.1871
## sit186  -99.081  215.43019   7.600086 -29.16485 -20.68588    1.3351
## sit187  -10.739  174.80713   0.009782 -28.98806 -21.72113    1.9512
## sit188 -371.775  340.18118  39.066487 -31.84536 -30.02903    1.5525
## sit189   -1.275 -100.45951  -0.155144  10.64242   5.36228   20.0842
## sit190  104.498  -90.24505 -11.660085  13.67643 -21.72238  -13.6856
## sit191   49.682  -58.90628  -3.856863   9.79648 -10.87969  -20.9930
## sit192  -38.059   50.75543   4.097134  -9.07128  -6.30040  -17.5248
## sit193   39.686   10.00863  -5.029459  -0.72998 -11.09028  -13.9031
## sit194  -24.374   25.80965   8.669958 -11.44561  29.93242 -208.1649
## sit195   28.567   48.86093  -5.432714 -16.57377  -2.17726 -163.0368
## sit196   36.396   24.90795  -4.617700  -0.78255 -15.08510    8.1206
## sit197  -52.304  112.75630   2.592293 -12.45947  -2.65620    6.3511
## sit198  -50.408   54.81634   2.861190   0.63958   3.59544    2.5988
## sit199  -62.692  108.71662   4.068208 -14.08929 -39.81954  -10.0865
## sit200  255.065 -156.09183 -24.895549  15.04885  -6.23792   -3.7339
## sit201  264.622   -4.74984 -29.532091 -15.37792 -11.60365    3.8816
## sit202  258.371  -29.25715 -26.765124 -12.05555 -11.32307    9.2298
## sit203  238.792  -10.46020 -27.024152 -13.37932   2.03940   -4.3624
## sit204  259.177    0.85563 -29.577947 -17.60335 -13.97539    4.6871
## sit205  266.620    8.98485 -30.666277 -20.92334  -9.31095    7.2478
## sit206  258.435   -7.49668 -28.653557 -17.94487  -8.53779    8.4609
## sit207   45.278   63.63532  -4.347834 -18.72115   9.07470   17.9615
## sit208  -49.401  131.62759   6.113839 -28.52431  17.30283   18.6096
## sit209  -24.380   87.24135   2.427142 -19.73245   6.39496   15.4812
## sit210   23.020   51.43123  -4.507207  -5.15230 -10.54634    7.8491
## sit211  205.962   -3.21608 -20.305432  -7.31003  13.41509   16.0238
## sit212  126.286  108.51427  -9.182775 -25.41918   6.86929   18.1483
## sit213   42.479   62.68688  -9.075301  -6.55234   2.66782    6.9099
## sit214  111.348  120.35204 -10.693135 -25.80335  -0.06565   11.6922
## sit215    8.398  165.92139   2.019080 -26.14306   3.00624   12.6842
## sit216  224.537  -67.66907 -23.569622   2.64693  -5.02341   11.2431
## sit217  226.077  -54.12527 -20.259016  -0.06004   0.34368   16.2590
## sit218   -8.513   50.41842   3.435057  -3.45504 -24.48045    7.4393
## sit219   79.231 -173.87110  -6.446185  30.52783  -4.57959  -15.0229
## sit220 -124.640 -326.82590  15.253766  86.83163 -18.66540   11.3164
## sit221  146.547 -245.73104 -15.937508  42.37643 -26.34633   -4.5219
## sit222    9.200    0.14379  -3.906001   4.69072  -9.96570  -11.4926
## sit223 -159.965  106.20688  20.575863 -19.49614  11.83958   -6.4980
## sit224  118.838 -432.75452 -13.687153  -4.21628 205.90425  -68.4184
## sit225   99.048 -341.05228 -12.790794   9.74765 118.86312   21.2562
## sit226 -664.639   66.90110  67.927235  53.58724 -13.49159  -19.9997
## 
## 
## Site constraints (linear combinations of constraining variables)
## 
##            RDA1     RDA2        PC1       PC2       PC3       PC4
## con1   -50.8672 -19.3308 -34.615566 -13.60594   5.80276   13.7411
## con2   -40.0456 -20.8854 -33.206407 -13.90277  28.98687   14.2441
## con3    10.2726 -11.9860 -26.026203 -14.25518  27.86951   28.7668
## con4   -13.4407 -17.6375 -30.372792   0.91158  17.05784   23.9373
## con5     3.3543  -7.5731 -18.029966 -12.93695  61.87626   14.8337
## con6    -2.7763 -35.8977 -29.027590  -8.82741 -18.19470    7.0818
## con7   -20.1168 -14.4738 -29.807868  -8.76044   8.65178   18.8549
## con8   -55.0452 -14.7904 -31.997624  -7.30947  72.30309   52.9857
## con9    17.6541  -7.4425 -26.346705  20.69576 -13.54343    9.8384
## con10   34.1069  -5.8276 -20.130961  57.99842  -8.90298   15.7071
## con11  -45.0506 -40.3300  16.117741 -26.87476  98.43987   75.5024
## con12   -2.0746 -32.7878  82.229273 -26.35216  21.68294   27.4388
## con13  -21.8543 -36.8509  35.002377  56.45269   8.80916   22.5313
## con14  -52.8478 -41.2367 -25.770464 -15.60207  -9.92309    8.8928
## con15   -4.7349 -12.9640  73.131123  -3.41385  54.55562   37.4839
## con16    7.5466 -41.3908  44.672582 -25.25865  69.94003   46.0739
## con17   -7.5320 -29.0801  63.664977 -16.04563  18.87576    4.0106
## con18   11.8363  12.3542 -26.549628 -15.13713  -5.35203    8.0530
## con19    5.2905  16.5208 -26.453954  -0.19923  -3.35171    8.2213
## con20   28.6603  -0.4923 -19.567087   1.94614  -9.74758    8.9149
## con21    2.1474  12.6762 -26.744408   9.54498  -5.42055    9.8875
## con22   30.2180  28.1613 -25.465689  -6.34791   5.48671   12.8965
## con23   33.4939   0.2650 -23.223544 -15.59292   1.03484   -7.9953
## con24   15.9534   8.9394 -27.221646 -10.46128  -3.33351    7.4014
## con25  -24.3356  36.7388  13.787954 -15.39409  -9.28190  -31.1128
## con26   -2.1435  20.6709  18.986389 -12.17421 -12.90751  -20.0969
## con27   17.9168  18.3966   0.014841   5.93332  -4.95943   -9.9670
## con28   -4.4157  20.8722   4.759508 -17.10916  -5.09428  -26.2970
## con29    0.1561 -19.0610  -3.226319 -21.87481  -5.57393   13.4324
## con30  -24.3495  -0.4621  19.944600 156.60820 -11.16897    8.6405
## con31    8.3111 -35.0923   2.904159  20.30124  -4.10274   20.8865
## con32  -29.6588   1.1384  21.743092  32.69740   1.64625    2.6726
## con33   -1.6866   6.4300  19.476734  15.38109   0.89773   11.5852
## con34   -8.6106   1.1535  33.649070  12.06702  21.64288   15.4412
## con35  -16.5187   1.2967   1.226710 -12.00154  -0.59756    8.8178
## con36  -29.1727   4.4393  -3.945567  -7.45852   0.42666    7.0758
## con37   -9.8226  -4.0574  11.873439  12.63759 -10.19570   11.7620
## con38  -28.9677 -42.9946 -17.275638  -8.04522 -12.76736    7.3408
## con39  -18.5720 -35.8922  66.536886 -18.52929  52.72174   42.6211
## con40   15.9607  -5.8407 -28.125225  -6.08364 -12.41944    7.8348
## con41   26.1459  11.2128 -22.182370  19.30244  -3.69415   13.6072
## con42  -42.8276   5.2914 -17.648094  65.74958 -18.14612   -1.9109
## con43    2.0194  25.6764  -8.587916  -0.67307  16.22716   -4.3074
## con44  -36.0523 -47.9333 -24.429569 -22.01992 -27.03666    0.5598
## con45  -45.8240 -51.5799 -39.619055 -29.19337 -41.65585   -6.9115
## con46   -1.7386 -37.9174 -23.390911 -20.96546 -18.22905    0.7580
## con47  -22.7995 -39.6311 -18.333495 -19.98780 -32.10103   -3.9865
## con48   35.2184   4.1825  -2.312555 -27.46073  -3.27428    4.5116
## con49   -3.5534 -25.2950 -20.027045  -0.76186 -18.49276  -12.9132
## con50  -44.0183 -50.4832 -37.102009 -29.28574 -39.68111   -9.2589
## con51   26.5463  12.1008 -20.414612   4.70982   0.26734   10.2154
## con52   30.5821   9.5266 -19.884322  -5.42725   0.81945   12.4216
## con53   30.7006  17.3176 -20.696022  27.45613   0.75272   15.5415
## con54   22.0989  14.0688 -24.969582  19.81113  -2.20130   10.0741
## con55   17.4766   9.0182 -22.005423  26.91560  -7.24850   11.6578
## con56  -27.3982  -6.2776 -24.902517  29.35027 -18.51768    0.4056
## con57   13.5366   9.7630 -21.843841  17.25672  -3.85881    9.0853
## con58   41.0614   6.8777 -21.159811  14.88563  -3.44153   13.9615
## con59   33.4855  12.1454 -15.260086  10.67650   0.40807    8.2649
## con60   18.3482   6.2042  -6.637246  15.11519  -1.02106   -2.5869
## con61   34.0780  14.1838  -1.853287 -11.27861  -2.26603    3.7574
## con62   27.2040  12.2361  -6.469645   1.31703   0.55604    8.6764
## con63   22.3975   1.2268  -9.800129  -2.56670 -10.03996    2.1925
## con64   31.5885  17.9705 -10.160265   3.30702  -1.22049    7.8079
## con65  -14.3093  41.5419   7.670754  22.03613   9.18441    1.5559
## con66  -17.2474  19.3298  52.746207 -15.05392  -5.98468   -6.0943
## con67  -34.4262 -23.4455  11.101474  10.31347 -32.31624    2.5416
## con68  -17.0357  -2.9267  14.912086   4.80104 -21.38739   -2.8086
## con69  -35.7113  20.2049  54.577673 -16.56418 -18.03473  -21.1904
## con70  -19.4888 -35.6286   6.679470  50.73995 -18.33752    7.0746
## con71   40.0554   5.8596  -5.912381  -3.68513  25.12218    9.1438
## con72   32.3494   5.8811  -2.402636 -11.18733   8.18624   12.9741
## con73    9.9421   4.9547  -5.281617 -20.07221  13.06854   16.5787
## con74   37.6573   6.7557  -8.327878 -17.66682   1.96552   11.0302
## con75    5.8621  41.6123  -8.084181 -10.48797  36.57652    2.2069
## con76   -5.2535  12.7277   1.354422 -28.17276 -14.58806    7.7783
## con77  -37.7393  57.0639  10.831780 -27.35963   1.21613    5.3263
## con78  -59.5483  92.0520   9.242539 -25.96804  12.56010   -3.2297
## con79  -41.7389  56.4592   1.185210 -26.75998   3.04667    1.0673
## con80  -16.1213 -22.3560   2.588156 -10.12348 -27.49221   -7.7807
## con81   -1.7807   1.6117  13.823415 -11.81992 -12.60819   -2.1429
## con82  -18.9251  31.3292  14.232097  -5.49700   4.73625  -10.9238
## con83   25.9113  14.0894   6.515954  -3.70180  -3.88126  -13.6804
## con84   48.3271 -26.7798   6.594701 -10.32944 -17.98949    2.3436
## con85  -22.8709  25.6378   2.658559 -27.99957  -9.73912  -12.1573
## con86  -14.4701  34.3125  -6.107252  16.73154  -6.69159    4.5667
## con87  -34.9707  43.5450 -21.096727  17.60074  -2.90268   -2.2684
## con88  -44.9519  52.6077 -13.912810  44.92209  -2.49988   -7.6995
## con89   30.3841   7.3855   3.639454  16.35456   4.90268   -5.2059
## con90   40.8130   2.6100   5.014643   1.24465   1.22981    6.9281
## con91   25.6738   6.2867  -0.875059  -2.78651   0.86674  -17.4551
## con92    4.1408   7.5678  -5.814775   0.95951 -11.04774    4.9160
## con93   49.8083  -3.9328   2.943887  -1.48965   5.26501  -37.0660
## con94  -23.9981  48.8653  -7.037579 -10.74776   0.16535    4.2906
## con95  -13.7434  66.9157  -9.258813 -12.70002  12.57049    3.9510
## con96   14.0214  65.6098   3.492950 -10.72180  11.71485    8.9775
## con97  -21.9623 -13.4527 -29.820213  33.04971 -14.48961  -11.3598
## con98  -39.5310 -22.8150 -25.662064 -13.87727 -21.84835   -9.9688
## con99   16.3842  -1.2022  18.760273 -18.37253 -21.00470    7.2870
## con100  23.6414 -34.2240  70.295616  38.93324 -34.89065   14.3496
## con101  16.9882  11.0919  21.024237  27.74468   0.20697  -10.7570
## con102  45.2643 -12.0167   4.232789  68.41588  -7.03167   -5.9667
## con103  27.4698 -30.6658 -10.834725  18.25573 -11.18394  -55.2803
## con104  41.0835 -12.1442 -18.069818  -3.32289   4.75440  -40.5778
## con105 -16.5377 -25.9330 -29.663812 -10.15060   7.66462   18.5437
## con106 -81.5423 -12.3700 -38.162957 -21.25874  16.30463   11.0192
## con107 -15.9481 -31.7450 -31.573637  -8.12855   8.16954   14.7728
## con108  23.3781  -1.6473 -14.741203  45.53799 -10.15558   14.3247
## con109  27.1177 -10.4740 -24.801573  34.35524 -12.75188   12.0942
## con110  -5.3595 -34.4539 -31.403832 -16.63648 -27.80567    2.0533
## con111  10.6276  -7.7079  86.041159 -24.10835  48.47036   38.5162
## con112  30.3252 -21.1917  62.738736  15.11165  76.33183   53.3936
## con113  -3.8419 -42.6501  84.192780 -37.02676 -47.60523    7.9333
## con114 -49.8698 -45.2584   3.526360 -21.53936 -19.65551   -1.5093
## con115   5.8455  -4.3583 -27.160029 -14.29068 -12.16914    6.9006
## con116  37.3065  11.1060 -23.618211   3.25286  -0.53172   13.5478
## con117  12.6488  36.9743 -22.855333  -6.25063   4.14047    8.5796
## con118  16.4599  -4.0944 -23.845995 -20.87065  -5.78601  -15.7749
## con119  19.5167  12.1887 -27.219621 -19.35483  -2.47223    9.5263
## con120 -18.5363  40.2783  30.292833  23.54810  -8.26241  -12.7457
## con121  15.9602  27.8934  32.024667   1.30952  -5.39993  -21.0686
## con122   8.8244  25.0714  18.691804  -9.99572  -4.53641  -45.7423
## con123 -10.0126  -2.0251  19.861858  -5.17628 -21.84975  -37.0437
## con124  17.1089  14.0033  27.490741 -23.19350  -9.51961  -53.1410
## con125  -4.5715 -34.6594  -0.716648  -2.63793 -18.25159   11.4839
## con126 -18.5482   0.2488  11.473843   1.92861   1.48166    9.4709
## con127 -22.8930 -25.6374 -16.203925  -7.91288 -11.57844    7.0135
## con128 -11.9048 -32.9040  -7.467265 -14.90368  -9.45420   12.8983
## con129  14.9744  14.7839  71.511922  -2.46936  -5.17572   12.4325
## con130  23.2344 -13.4719  -7.991360  14.53331  26.57292    8.7169
## con131  10.0929  24.9130 -10.850356  41.37221  27.91371   12.3642
## con132  10.6934  19.5971  -9.782928  27.85297  34.18142    8.6709
## con133  52.1122 -27.1862   2.292207 -16.92975   4.09174   10.6937
## con134  -9.2322 -39.5724 -25.582320 -12.62906 -14.98924  -10.3041
## con135  39.0881  12.7660  -6.503890  -8.62349   7.63677   -1.2157
## con136 -19.4104 -44.3196 -16.622612   3.39158  -7.88948  -48.0986
## con137 -39.7964 -18.8100 -17.146464  66.20047  98.65932 -105.2963
## con138 -42.5427 -16.3214 -17.658369  53.00357  86.13424  -56.0367
## con139 -16.7225 -33.3769 -16.694791  48.24575  23.37100  -39.7338
## con140   9.5500 -14.8150  97.810182  13.15595 -36.43033    5.9120
## con141  15.1722   4.3365  96.835422  58.20712 -28.36290   10.4112
## con142  27.8100  -0.2929  90.925602  19.86314 -19.48343    9.6463
## con143  22.0787   7.4190 -19.505826  26.16411  -5.81701   10.8893
## con144  14.4234   0.5148 -26.330919   6.37332  -8.72407    8.8134
## con145  42.0740  14.2614 -19.890729  15.29298  -1.43271   13.3713
## con146  20.5124   3.3048  -7.359657   2.49753  -6.69355    8.0563
## con147  15.0136 -16.3297 -12.221939  14.99642 -15.89430    0.8485
## con148  26.9032  17.5814 -10.885680  27.55135  -0.33512    6.8980
## con149 -18.7370 -23.1078  47.089023 -13.76842 -23.58467   -7.0546
## con150 -16.9150 -12.1086  21.005865   2.08384 -26.57443    5.0691
## con151  -8.4219  -1.3740  21.998787   6.09403 -13.37921   -6.2058
## con152 -17.8845  -1.7370   8.511512 -22.39173 -21.69025    5.1730
## con153   2.9111   2.4966   2.115569 -28.56064 -12.34334    9.7445
## con154 -13.8004 -36.4140  -4.329275 -22.09204  -1.71271    8.7424
## con155 -17.4239  -1.1967  12.132166  39.31498  -0.97049   -1.8407
## con156 -14.5278   3.5430  15.191323   6.64274  -4.08982   -1.7061
## con157 -31.1120 -29.4852  23.074373 -25.35972   9.53387  -51.1322
## con158  25.1934 -17.9472  33.483273 -31.39909   9.33721  -82.7049
## con159  33.8900  10.3464  21.875580 -28.94439  34.80909  -62.7164
## con160 -28.2199 -38.5365   1.804031  83.38880 -32.14035    7.0966
## con161 -32.9483 -46.4971  -5.047810  -5.03074  16.59717   31.2890
## con162   5.7627  -3.6592  22.705748 -22.39116  11.41905   -6.4365
## con163  -3.8223 -36.2832  24.074962  -9.93871  48.03313   35.8123
## con164  28.1997  -2.0934  -2.579173 -13.30088   7.37183   11.7534
## con165  55.6730  13.0502  -7.857260 -15.04168   5.55472   15.3621
## con166  55.3919  -3.7384   2.524112 -27.78782  -8.83197   15.0032
## con167   6.7137  14.6096 -16.685978 -26.48838  -8.03291    8.6156
## con168   2.8745   5.6754 -12.119048 -27.35236 -12.48597    5.6134
## con169   8.5570   1.6363  -1.332440 -20.43590  -3.64103    2.9770
## con170 -16.0151   2.7605   7.300025 117.54326  -6.43064   10.9739
## con171  -6.3084  12.2706  -6.940609 -27.59761  -3.57134    1.7760
## con172  -1.9391 -29.1677  38.465005 -26.71037 -33.26481  -22.5049
## con173  34.0137 -15.3841  41.205348 -31.68503 -22.21377  -20.8197
## con174 -16.0426  25.1058  53.471437 -31.64695 -18.27292  -21.9065
## con175  10.7940  42.3793  12.250673 -27.16387   3.38871    7.9124
## con176 -76.7693  93.5442  -2.888454 -25.35997  12.00751    2.4280
## con177  24.5370  -1.8871  19.063073 -29.57447 -12.61085    4.4836
## con178 -13.4068  -2.5705 -20.225802 -27.50555 -15.97260    5.4895
## con179   0.1651  22.1705   1.625452  -4.54566  -5.72596   -1.8197
## con180 -38.1120 -26.7069  -3.015140 -12.58333 -31.83507  -12.4073
## con181 -15.6181   1.0314   5.817779 -17.18569 -18.97604   -9.3211
## con182   7.2918  12.2618 -16.638184  49.22149  -8.02597    6.1885
## con183 -40.9395  35.8162 -18.770932  35.40456  -6.90481   -0.8337
## con184 -59.7101  59.8579 -18.009435   7.20354  -1.50738   -4.1342
## con185  34.1501   6.6236 -20.256920   2.60538   4.17146   10.1871
## con186 -32.7884  13.8440   7.600086 -29.16485 -20.68588    1.3351
## con187 -11.3937  -3.0801   0.009782 -28.98806 -21.72113    1.9512
## con188 -28.1264   4.8249  39.066487 -31.84536 -30.02903    1.5525
## con189  -2.3909 -39.0423  -0.155144  10.64242   5.36228   20.0842
## con190   1.6674 -27.2578 -11.660085  13.67643 -21.72238  -13.6856
## con191  15.7135 -19.9385  -3.856863   9.79648 -10.87969  -20.9930
## con192  -2.1590 -18.0860   4.097134  -9.07128  -6.30040  -17.5248
## con193  -4.8069   3.2242  -5.029459  -0.72998 -11.09028  -13.9031
## con194  52.1201  -5.9690   8.669958 -11.44561  29.93242 -208.1649
## con195 -19.7263 -18.5373  -5.432714 -16.57377  -2.17726 -163.0368
## con196  -4.5001   8.8413  -4.617700  -0.78255 -15.08510    8.1206
## con197 -29.6879  40.6689   2.592293 -12.45947  -2.65620    6.3511
## con198 -25.1239  55.1197   2.861190   0.63958   3.59544    2.5988
## con199 -27.4944 -43.7694   4.068208 -14.08929 -39.81954  -10.0865
## con200  35.7050 -14.1573 -24.895549  15.04885  -6.23792   -3.7339
## con201   3.7853  -1.8731 -29.532091 -15.37792 -11.60365    3.8816
## con202  21.9998 -19.3922 -26.765124 -12.05555 -11.32307    9.2298
## con203   0.2506  18.7842 -27.024152 -13.37932   2.03940   -4.3624
## con204  -2.1292 -11.1524 -29.577947 -17.60335 -13.97539    4.6871
## con205  -4.2909  -6.4140 -30.666277 -20.92334  -9.31095    7.2478
## con206   5.3361 -13.8142 -28.653557 -17.94487  -8.53779    8.4609
## con207   6.7116   6.0521  -4.347834 -18.72115   9.07470   17.9615
## con208   4.2111   8.2819   6.113839 -28.52431  17.30283   18.6096
## con209  -3.2458  -2.2803   2.427142 -19.73245   6.39496   15.4812
## con210 -16.9301  22.6142  -4.507207  -5.15230 -10.54634    7.8491
## con211  26.9094  54.0742 -20.305432  -7.31003  13.41509   16.0238
## con212  44.9391  30.9500  -9.182775 -25.41918   6.86929   18.1483
## con213 -37.6406  66.9039  -9.075301  -6.55234   2.66782    6.9099
## con214  16.5961  33.1185 -10.693135 -25.80335  -0.06565   11.6922
## con215  25.7832  41.3046   2.019080 -26.14306   3.00624   12.6842
## con216  16.6731  13.5362 -23.569622   2.64693  -5.02341   11.2431
## con217  47.4213  13.2304 -20.259016  -0.06004   0.34368   16.2590
## con218  21.4605 -21.9911   3.435057  -3.45504 -24.48045    7.4393
## con219  22.8607 -15.5595  -6.446185  30.52783  -4.57959  -15.0229
## con220  11.2084   2.1087  15.253766  86.83163 -18.66540   11.3164
## con221   6.4511 -39.7303 -15.937508  42.37643 -26.34633   -4.5219
## con222 -25.2783  17.7636  -3.906001   4.69072  -9.96570  -11.4926
## con223  21.2772 -32.1930  20.575863 -19.49614  11.83958   -6.4980
## con224   0.2383 -29.9440 -13.687153  -4.21628 205.90425  -68.4184
## con225 -12.2987 -33.1548 -12.790794   9.74765 118.86312   21.2562
## con226 -64.8027  73.2901  67.927235  53.58724 -13.49159  -19.9997
## 
## 
## Biplot scores for constraining variables
## 
##               RDA1   RDA2 PC1 PC2 PC3 PC4
## weight    -0.09769 0.9952   0   0   0   0
## thickness  0.41362 0.9104   0   0   0   0

#Plot
# Plot the RDA biplot
plot(rda_result, scaling = 3)

# Add labels to the plot
text(rda_result, display = "species", col = "blue", cex = 0.8) #species - environmental variales
text(rda_result, display = "sites", col = "red", cex = 0.8) # the sites - response variables 

# Add a title to the plot
title(main = "Redundancy Analysis (RDA) Biplot")